Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Koncepcijsko projektiranje inteligentnog unutarnjeg transporta materijala korištenjem umjetne inteligencije

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matlab© software package is used for developing genetic algorithms, manufacturing process simulation, implementing search algorithms and neural network training. The obtained paths are tested by means of the Khepera II mobile robot system within a static laboratory model of manufacturing environment. The experiment results clearly show that an intelligent mobile robot can follow paths generated by using genetic algorithms as well as learn and predict optimal material transport flows thanks to using neural networks. The achieved positioning error of the mobile robot indicates that the conceptual design approach based on the axiomatic design theory can be used for designing the material transport and handling tasks in intelligent manufacturing systems.Pouzdan i efikasan transport materijala je jedan od ključnih zahtjeva koji utječe na povećanje produktivnosti u industriji. Iz tog razloga, u radu su predložena dva pristupa za inteligentan transport materijala korištenjem mobilnog robota. Prvi pristup se zasniva na primjeni genetskih algoritama za optimizaciju tehnoloških procesa. Optimalna putanja se dobiva korištenjem optimalnih tehnoloških procesa i genetskih algoritama za vremensko planiranje, uz minimalno vrijeme kao kriterij. Drugi pristup je temeljen na primjeni teorije grafova za generiranje putanja i neuronskih mreža za učenje generirane putanje. Matlab© softverski paket je korišten za razvoj genetskih algoritama, simulaciju tehnoloških procesa, implementaciju algoritama pretraživanja i obučavanje neuronskih mreža. Dobivene putanje su testirane pomoću Khepera II mobilnog robota u statičkom laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju kako inteligentni mobilni robot prati putanje generirane korištenjem genetskih algoritama, kao i da uči i predviđa optimalne tokove materijala zahvaljujući neuronskim mrežama. Ostvarena pogreška pozicioniranja mobilnog robota ukazuje da se koncepcijski pristup baziran na aksiomatskoj teoriji projektiranja može koristiti u projektiranju transporta i manipulacije u inteligentnom tehnološkom sustavu

An enhanced ant colony optimization approach for integrated process planning and scheduling

An enhanced ant colony optimization (eACO) meta-heuristics is proposed in this paper to accomplish the integrated process planning and scheduling (IPPS) in the jobshop environments. The IPPS problem is graphically formulated to implement the ACO algorithm. In accordance with the characteristics of the IPPS problem, the mechanism of eACO has been enhanced with several modifications, including quantification of convergence level, introduction of pheromone on nodes, new strategy of determining heuristic desirability and directive pheromone deposit strategy. Experiments are conducted to evaluate the approach, while makespan and CPU time are used as measurements. Encouraging results can be seen when comparing to other IPPS approaches based on evolutionary algorithms. © 2013 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.published_or_final_versio

Solving integrated process planning and scheduling problem with constraint programming

Session - Scheduling and Sequencing 3: paper no. T3D3The APIEMS 2012 Conference proceedings' website is located at http://apiems.net/conf2012/Process planning and scheduling are two important manufacturing functions which are usually performed sequentially. However, due to the uncertainties and disturbances frequently occurring in the manufacturing environment, the separately conducted process plan and shopfloor schedule may lose their optimality, becoming ineffective or even infeasible. Researchers have considered the potential of integrated process planning and scheduling (IPPS) to conduct the two manufacturing planning activities concurrently instead of sequentially. That is, to integrate process planning with dynamic shopfloor scheduling to cope with the realtimeshopfloor status. The IPPS problem is very complex and it has been regarded as an NP-hard problem. Many researchers have attempted to solve the IPPS problem with intelligent approaches such as meta-heuristics and agent-based negotiation. In this paper, a constraint programming-based approach is proposed and implemented in the IPPS problem domain. Constraint programming (CP) features great modeling capabilities to reflect complex constraints of a problem, and there is a great potential for CP to be used to solve IPPS problems. The approach is implemented and tested on the IBM ILOG platform, and experimental results show that the CP can handle the IPPS problem efficiently and effectively.published_or_final_versio

Integrated process planning and scheduling using genetic algorithms

Projektiranje tehnoloških procesa i planiranje predstavljaju dvije najvažnije funkcije svakog proizvodnog procesa. Tradicionalno se one smatraju dvjema odvojenim funkcijama. U ovom se radu predlaže Genetički Algoritam (GA) za integraciju ovih aktivnosti, gdje se simultano odvija izbor najboljeg tehnološkog procesa i vremenski plan poslova u pogonu. U radu se za rješavanje te vrste problema predstavlja pristup zasnovan na proračunskoj tablici neovisnog područja. U modelu se razmatraju odnosi prvenstva u izvođenju poslova na temelju kojih se donosi implicitno predstavljanje mogućih planova za izvršenje svakog posla. Zbog provjere izvršenja i ostvarivosti predstavljenog pristupa, predloženi se algoritam provjeravao na nizu referentnih problema prilagođenih iz ranije objavljene literature. Eksperimentalni rezultati pokazuju da se predloženim pristupom mogu učinkoviti postići optimalna ili njima blizu rješenja za probleme prilagođene iz literature. Također je pokazano da predloženi algoritam ima opću namjenu i može se primijeniti za optimizaciju bilo koje objektivne funkcije bez promjene modela ili osnovne GA rutine.Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine

Integration of process planning, scheduling, and mobile robot navigation based on triz and multi-agent methodology

U radu je predstavljena metodologija za razvoj softverske aplikacije za integraciju projektovanja tehnološkog procesa, terminiranja proizvodnje i navigacije mobilnog robota u tehnološkom okruženju. Predložena metodologija je bazirana na primeni teorije inventivnog rešavanja problema i multiagentske metodologije. Matrica kontradikcije i inventivni principi su se pokazali kao efektivan alat za otklanjanje kontradiktornosti u koncepcijskoj fazi razvoja softvera. Predložena multiagentska arhitektura sadrži šest agenata: agent za delove, agent za mašine, agent za optimizaciju, agent za planiranje putanje, agent za mašinsko učenje i agent mobilni robot. Svi agenti zajedno učestvuju u optimizaciji tehnološkog procesa, optimizaciji planova terminiranja, generisanju optimalnih putanja koje mobilni robot prati i klasifikaciji objekata u tehnološkom okruženju. Eksperimentalni rezultati pokazuju da se razvijeni softver može koristiti za predloženu integraciju, a sve u cilju poboljšanja performansi inteligentnih tehnoloških sistema.This paper presents methodology for development of software application for integration of process planning, scheduling, and the mobile robot navigation in manufacturing environment. Proposed methodology is based on the Russian Theory of Inventive Problem Solving (TRIZ) and multiagent system (MAS). Contradiction matrix and inventive principles are proved as effective TRIZ tool to solve contradictions during conceptual phase of software development. The proposed MAS architecture consists of six intelligent agents: job agent, machine agent, optimization agent, path planning agent, machine learning agent and mobile robot agent. All agents work together to perform process plans optimization, schedule plans optimization, optimal path that mobile robot follows and classification of objects in a manufacturing environment. Experimental results show that developed software can be used for proposed integration in order to improve performance of intelligent manufacturing systems